CROSS-REFERENCE TO RELATED APPLICATIONS
BACKGROUND
[0002] This disclosure relates to the monitoring of motion, breathing, heart rate and sleep
state of humans in a convenient and low-cost fashion, and more particularly to an
apparatus, system, and method for acquiring, processing and displaying the corresponding
information in a easily understandable format.
[0003] Monitoring of sleep patterns, heart rate and respiration during sleep is of interest
for many reasons from clinical monitoring of obstructive and central sleep apnea in
both adults and young children, to ensuring healthy sleep patterns in young babies.
For example, infants which are born prematurely often have immature cardiorespiratory
control which can cause them to stop breathing for 15-20 seconds, or to breathe shallowly.
This is referred to as apnea of prematurity, and often persists for two to three months
after birth. Periodic breathing (in which the amplitude of respiration rises and falls
over several minutes) is also common in babies born prematurely. In such infants,
it is also useful to monitor heart rate as a low heart rate (bradycardia) can be used
as a warning signal that the baby is not receiving sufficient oxygen.
[0004] In adults, common sleep disordered breathing syndromes include obstructive sleep
apnea and central sleep apnea. In obstructive sleep apnea, the upper airway collapses,
restricting the flow of air to the lungs, even in the presence of ongoing respiratory
effort. Obstructive sleep apnea can also cause characteristic changes in heart rate,
which may be detrimental to the subject. Obstructive sleep apnea has a high prevalence
in the adult population, affecting about 2-4% of adults over the age of 40. Obstructive
events lead to a reduced flow of air to the lungs, and subsequently a lowering of
oxygen level in the blood. Central sleep apnea is less common than obstructive sleep
apnea in adults, and is distinguished by a complete loss of respiratory effort, which
leads to a loss of air to the lungs, and eventually a lowering of oxygen in the blood.
In both central and obstructive sleep apnea, the body's natural defense mechanisms
will be stimulated by the oxygen desaturation, and eventually increase respiratory
effort sufficient to restore airflow. However, this is often accompanied by an arousal
(which can be observed in the person's electroencephalogram) which either wakes the
person up momentarily, or brings them into a lighter stage of sleep. In either event,
the person's sleep is disrupted, and they experience poor quality sleep, which often
leads to excessive daytime sleepiness.
[0005] Other common sleep disorders in adults, whose effects are not related to respiration
are Periodic Limb Movements Disorder (PLMD) and Restless Legs Syndrome (RLS). In PLMD,
a subject makes characteristic repetitive movements (usually of the leg) every 30-40
seconds, leading to sleep disruption due to frequent awakenings. In RLS, the subject
has an overwhelming desire to move or flex their legs as they fall asleep, again leading
to disrupted sleep patterns. Monitoring of these unusual body movements is important
to confirming the diagnosis of these conditions and initiating treatment.
[0006] The most common adult sleep disorder is insomnia which is defined as a difficulty
in initiating or maintaining sleep. Chronic insomnia is estimated to affect about
10% of the American population. However, at present full clinical evaluation of sleep
patterns relies on electroencephalograph (EEG) monitoring, often requiring a hospital
stay. There is a need for simpler methods of assessing sleep patterns for adults in
the home environment. For example, evidence has shown that sleep deprivation adversely
alters the balance of leptin and ghrelin, two hormones which are significantly involved
with the body's appetite control system. Voluntary sleep deprivation over a period
of time (due to lifestyle choice) has been correlated with increased Body-Mass-Index
(an indicator of obesity). Hence, objective measurement and control of sleep patterns
may play a role in weight management.
[0007] Moreover, sleep is of particular important to young children. Infants spend more
time asleep than awake in their first three years, emphasizing its crucial importance
in development. Sleep is important for physical recuperation, growth, maturing of
the immune system, brain development, learning, and memory. Conversely, infants who
do not receive sufficient sleep or who sleep poorly often display poor mood, as well
as having an adverse effect on their parents' sleep patterns. Indeed it is estimated
that 20-30% of children under the age of 3 years have common sleep problems such as
frequent night-wakings, and difficulty falling asleep on their own. Studies have shown
that parents can help their babies achieve good sleep patterns through a variety of
behavioral approaches. A non-invasive safe sleep monitor can assist in adopting such
behavioral approaches. Automated collection of sleep information can help parents
in assuring their children are sleeping adequately. For example, a system which monitors
night-time sleep and daytime naps can provide information in the form of a visual
sleep log which can be stored and visualized over a period of time (e.g., using a
world wide web interface on a personalized page). The sleep monitor can also track
sleep fragmentation (e.g., frequent awakenings during night-time sleep), which is
correlated with infant contentment. Finally, characteristic changes in breathing,
heart rate, and movement may be associated with night-time urination and defecation
in infants, and hence can be used to alert parents to change diapers.
[0008] In adults, measurements of heart rate and breathing rate during sleep can be used
as clinical markers for continuous health monitoring. For example, elevated breathing
rates can be linked to forms of respiratory distress or diseases such as chronic obstructive
pulmonary disease which require increased respiratory effort. It has been shown in
clinical studies that a particular type of breathing pattern, referred to as Cheyne-Stokes
respiration or periodic breathing, is a marker for poor prognosis in people with heart
disease. Simultaneous measurement of respiration and cardiac activity can also allow
evaluation of a phenomenon called respiratory sinus arrhythmia (RSA) in which the
heart rate speeds up and slows down in response to each breath. The amplitude of this
coupling effect is typically stronger in young healthy people, and therefore can be
used as another health marker. Heart rate changes during sleep can also provide useful
clinical information - elevated heart rates can be an indicator of systemic activation
of the sympathetic nervous system, which can be associated with sleep apnea or other
conditions. Furthermore, a common clinical problem is to monitor response to treatments
aimed at stabilizing heart rhythm. For example, a common cardiac arrhythmia is atrial
fibrillation (AF), in which the upper chambers of the heart beat irregularly. Consequently
the heart rate is irregular and elevated. Common treatments for AF include pharmacological
and surgical approaches, and a goal of the doctor is to provide follow-up monitoring
to look for a reoccurrence of the arrhythmia. Non-invasive low-cost monitoring of
heart rate during sleep is a useful mechanism to provide doctors with a means of providing
such monitoring follow-up for this condition, and other cardiac arrhythmias.
[0009] Accordingly, a method, system or apparatus which can reliably monitor sleep patterns,
breathing and heart rate during sleep, and motion during sleep would have utility
in a variety of settings.
[0010] A variety of techniques have been disclosed in the background art for addressing
the need for respiratory, cardiac and sleep monitoring. Respiratory monitoring is
currently carried out primarily in a hospital environment using a variety of approaches.
A common method for measuring respiratory effort uses inductance plethysmography,
in which a person wears a tightly fitting elastic band around their thorax, whose
inductance changes as the person breathes in and out. This technique has become the
most widely used respiration monitoring technique in sleep medicine. A severe limitation
of the method from a convenience point of view is that the person has to wear a band,
and remains connected to the associated electronic recording device via wires.
[0011] An alternative system for measuring respiratory effort is to use impedance pneumography,
in which the impedance change of the thorax is measured. This technique is often used
in clinical infant apnea monitors, which generate an alarm in a baby monitor when
no breathing is detected. In order to detect the breathing signal, electrodes must
be attached to a sleeping infant. More generally, there are a number of commercial
products available which use impedance measurements across the baby's chest to detect
central apnea (e.g., the AmiPlus Infant Apnea Monitor produced and marketed by CAS
Medical Systems). The limitation of this technology is that it requires electrodes
to be attached to the baby, has an active electrical component, and needs to be used
with caution as the wires can cause strangulation if not properly fitted.
[0012] Heart rate during sleep can be measured using conventional surface electrocardiogram
measurements (typically referred to as a Holter monitor), in which a person typically
wears three or more electrodes. A limitation of this method is the need to wear electrodes
and the associated electronic recording device. Heart rate fitness monitors record
heart rate by also measuring surface electrocardiogram, typically using a wearable
chest band which has integrated electrodes. Again, there is the need to wear the device
and also the accompanying signal collector (typically a wrist watch style device).
Heart rate during sleep can also be measured using pulse oximetry, in which a photoplethysmogram
is collected at the finger or ear. There is a characteristic variation in the pulse
photoplethysmogram signal which corresponds to each beat of the heart.
[0013] Integrated systems for collecting heart rate and respiration using combinations of
the techniques discussed above for heart rate and respiratory effort have been developed.
In one commercial product, contact ECG and inductance plethysmograph sensors have
been embedded in a custom-designed jacket. The cost of providing such a wearable system
is relatively high, and the system requires contact sensors.
[0014] One indicator of sleep status is the degree of motion while lying down. Motion during
sleep can be detected by wrist-worn accelerometers, such as those commercially marketed
by MiniMitter as "Actiwatch®". These use microelectronic accelerometers to record
limb movement during sleep. A limitation of this technology is the requirement for
the individual to wear a device, and the fact that it is not integrated with simultaneous
breathing and cardiac monitoring, which limits the physiological usefulness of such
measurements. Motion can also be detected using under-mattress piezoelectric sensors,
which produce a voltage spike when pressure is applied to the mat, and hence can detect
movement.
[0015] Various approaches to measuring heart rate, respiration, and motion in a non-contact
fashion have been described. One approach is to use optical interferometry to provide
a non-contact method for determining respiration, cardiac activity and motion. However,
a limitation of their invention is that the optical signals are blocked by clothes
or bedding materials. The processing required to obtain and differentiate breathing,
cardiac and motion elements is unclear. A second approach is to use ultrasonic waves
to detect motion. A limitation of this approach is that signal-to-noise ratio can
be poor due to low reflection, and respiration, motion and cardiac signals can not
be collected simultaneously. A further non-contact measurement technique for assessing
bodily motion is to use continuous wave radar (using electromagnetic radiation in
the radio frequency range) in detecting respiration and
US 2005/073424 A1 discloses a method for sensing information about the position and/or movements of
the body of a living being, using a radar system with pulsed electromagnetic waves
to determine heart beat and breathing motion of a vehicle driver.
[0016] Limitations of previous methods to obtain physiological data using these non-contact
methods include various sensor limitations (e.g., obstruction by bed clothes, poor
signal-to-noise ratios, or the need for too large an antenna). Furthermore, the background
art does not provide methods for extracting useful "higher-level" physiological status,
such as breathing rate, cardiac rhythm status, sleep state, respiratory distress,
or evidence of sleep disturbed breathing. The current disclosure also possesses advantages
related to the fact that it requires very low levels of transmitted radio-frequency
power (e.g., less than 0dBm), can be made in a small size (e.g., the sensor can be
5 cm x 5cm x 5cm or less in size), can be battery powered, and is safe for human use.
SUMMARY
[0017] This disclosure provides various embodiments of an apparatus, system, and method
for monitoring of motion, breathing, heart rate and sleep state of humans in a convenient
and low-cost fashion. In various embodiments, a sensor unit suitable for being placed
close to where the subject is sleeping (e.g., on a bedside table) may be interfaced
with a monitoring and display unit where results can be analyzed, visualized and communicated
to the user. The sensor unit and the display/monitoring unit can be incorporated into
a single stand-alone unit, if desired. The unit may include one or more of a non-contact
motion sensor (for detection of general bodily movement, respiration, and heart rate);
a processing capability (to derive parameters such as sleep state, breathing rate,
heart rate, and movement); a display capability (to provide visual feedback); an auditory
capability (to provide acoustic feedback, e.g., a tone whose frequency varies with
breathing, or an alarm which sounds when no motion is detected); a communications
capability (wired or wireless) to transmit acquired data to a separate unit. This
separate unit can carry out the processing, display and auditory capability mentioned
above, and can also be a data logger.
[0018] The apparatus useful in detecting, analyzing, and displaying one or more of respiration,
cardiac activity, and bodily function or movement of a subject, includes a processor
configured to analyze a signal reflected from the subject without physical contact
with the subject and to derive measurements of said one or more of respiration, cardiac
activity, and bodily function or movement therefrom; and a display configured to provide
the analyzed and derived measurements to a local or remote user of the apparatus.
[0019] The system for measuring, analyzing, and displaying one or more of a respiration
parameter, cardiac activity, and bodily movement or function of a subject includes
a transmitter arrangement configured to propagate a radio frequency signal toward
the subject; a receiver arranged to receive a radio-frequency signal reflected from
the subject; a processor arranged to analyze the reflected signal to produce measurements
of one or more of a respiration parameter, cardiac activity, and a bodily movement
or function, and a monitor to provide selected information to a local or remote user
of the system by either an audible or visual indication, or both.
[0020] The method for measuring, analyzing, and displaying one or more physiological parameters
of a subject includes the steps of sensing a signal reflected from the subject; processing
and analyzing the reflected signal; deriving said one or more physiological parameters
pertaining to said subject, said one or more physiological parameters comprising one
or more of a respiration parameter, cardiac activity, and bodily movement or function
of a subject; and making selected derived information available to a user.
[0021] Additional sensing capabilities may be added to the sensor unit, including a sound
sensor; a sensor for measuring body temperature from a distance (infrared); and sensors
for environment humidity, temperature and light level.
[0022] The processing capability extracts information relating specifically to the separate
breathing, heart rate, and motion components, and uses this raw information to derive
higher level information such as sleep state, presence of sleep disordered breathing,
cardiac arrhythmias, and sleep disturbance. The display capability provides a means
for clearly communicating this physiological information in a clearly understandable
fashion, such as providing a simple color indicator to indicate sleep status (awake
or asleep). The processing capability can also incorporate measurements from the auxiliary
sesnors, which allows the derivation of physiological information about coughing,
wheezing, and other respiratory disturbances.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Embodiments of the disclosure will now be described with reference to the accompanying
drawings in which:
FIG. 1 is a diagram illustrating a schematic of the radio frequency sensor components
of the system, with a pulsed continuous wave signal for illustration;
FIG. 2 is a diagram illustrating a schematic of how a raw sensor signal can be processed
to produce three signals for further processing;
FIG. 3 is a diagram illustrating a more detailed view of a way by which the raw sensor
signal can be processed to yield motion information;
FIG. 4 is a diagram illustrating sample signals acquired from the system for respiratory
activity, in comparison with the signals obtained from a conventional standard inductance
plethysmography (using a commercial system called Respiband®);
FIG. 5 is a diagram illustrating sample signals acquired from the system for cardiac
activity in comparison with the signals obtained from an conventional heart rate monitoring
system based on a pulse oximeter;
FIG. 6 is a diagram illustrating techniques by which the system may calculate heart
rate;
FIG. 7 is a diagram illustrating how information may be integrated from the derived
motion m(t), respiratory r(t) and cardiac signals c(t) together to extract meaningful
physiological classifications, by using a classifier model;
FIG. 8 is a diagram illustrating an example of an output displayed in one embodiment;
FIG. 9 is a diagram illustrating how the apparatus and system of this disclosure can
be used in a wireless communications configuration where the processing and display
unit are remote from the sensor unit;
FIG. 10 is a diagram illustrating how information may be integrated from the derived
motion m(t), respiratory r(t) and cardiac signals c(t) together to extract an Apnoea-Hypopnoea
index (AHI) by using a classifier model;
FIG. 11 is a diagram illustrating an algorithm for processing any combination of the
breathing signal, heart-rate and movement signal to form an estimated AHI including
using only measured and/or derived respiratory effort of a human subject;
FIG. 12 illustrates an example output of the epoch labels of apnea estimated from
the breathing signal from a night time recording of a subject for which the estimated
AHI was 2.9 and the expert determined AHI was 4;
FIG. 13 is a block diagram of another embodiment of the apparatus and system of this
disclosure illustrating auxiliary sensors; and
FIG. 14 provides a non-contact sensor recording for Record Number 2 (top axis) with
the actimetry recording on the bottom axis in which the signals have been aligned
and truncated, and in which the middle axis shows the non-contact signal mapped to
actimetry.
DETAILED DESCRIPTION
[0024] FIG. 1 is a diagram illustrating a schematic of the radio frequency sensor components
of the apparatus and system, with a pulsed continuous wave signal. The transmitter
transmits a radio-frequency signal towards a subject, e.g., a human. The reflected
signal is then received, amplified and mixed with a portion of the original signal,
and the output of this mixer is then low pass filtered. The resulting signal contains
information about the movement, respiration and cardiac activity of the person, and
is referred to as the raw sensor signal. In an alternative embodiment, the system
may also use quadrature transmission in which two carrier signals 90 degrees out of
phase are used. In the limits that the pulse becomes very short in time, such a system
can be characterized as an ultrawideband (UWB) radio-frequency sensor.
[0025] FIG. 2 is a diagram illustrating a schematic of how the raw sensor signal can be
processed to produce three signals for further processing. The raw signal generally
will contain components reflecting a combination of bodily movement, respiration,
and cardiac activity. Bodily movement can be identified by using zero-crossing or
energy envelope detection algorithms (or more complex algorithms), and used to form
a "motion on" or "motion off' indicator. The respiratory activity is typically in
the range 0.1 to 0.8 Hz, and can be derived by filtering the original signal with
a bandpass filter whose passband is in that region. The cardiac activity is reflected
in signals at higher frequencies, and this activity can be accessed by filtering with
a bandpass filter with a pass band such as 1 to 10 Hz.
[0026] FIG. 3 is a diagram illustrating a more detailed view of the means by which the raw
sensor signal can be processed to yield motion information. One technique calculates
the energy envelope of the signal over a period of time, and periods which have a
high energy envelope by comparison with a threshold are determined to be periods of
motion. A second technique counts the number of times the signal crosses a threshold
(e.g., the zero value) and areas with a high value of zero-crossing are determined
as being high motion areas. These techniques can be used separately or in combination
to achieve a motion detection.
[0027] FIG. 4 is a diagram illustrating sample signals acquired from the system for respiratory
activity, in comparison with the signals obtained from the current clinical gold standard
of inductance plethysmography (using a commercial system called Respiband®). The disclosed
apparatus and system are capable of measuring both the amplitude and frequency of
breathing.
[0028] FIG. 5 is a diagram illustrating sample signals acquired from the apparatus and system
for cardiac activity, in comparison with the signals obtained from a conventional
heart rate monitoring system based on a pulse oximeter. The disclosed system is capable
of acquiring signals in which individual heart beats can be distinguished.
[0029] FIG. 6 is a diagram illustrating techniques by which the apparatus and system may
calculate heart rate. Cardiac activity causes a pressure wave at the surface of the
body called the ballistocardiogram. In some cases (due to a combination of positioning,
body type, and distance form the sensor), the cardiac signals will provide a signal
in which individual pulses can be clearly seen. In such cases, heart beats will be
determined by a threshold passing technique (a pulse is associated with the point
where the signal exceeds the threshold). In more complex (but typical cases), the
ballistocardiogram will present a more complex but repeatable pulse shape. Therefore
a pulse shape template can be correlated with the acquired cardiac signal, and places
where the correlation is high will be used as the heart beat locations.
[0030] FIG. 7 is a diagram illustrating how the invention integrates information from the
derived motion m(t), respiratory r(t) and cardiac signals c(t) together to extract
meaningful physiological classifications, by using a classifier model. The three streams
of data are segmented into time epochs, and statistical features are generated for
each epoch. For example, these features might be the signal variance, spectral components,
or peak values, and these are grouped into vectors
Xr, Xn, and
Xc. The vectors can then form a single vector
X of features. These features are combined (for example in a linear weighted fashion
using α
TX) to determine the probability that the epoch corresponds to a certain physiological
state (e.g., person asleep, person awake). The classification from epochs can be further
combined with classification from other epochs to form higher level decisions (such
as whether the person is in REM, NONREM, or WAKE states).
[0031] FIG. 8 is a diagram illustrating an example of outputs displayed in one embodiment.
A light emitting diode may be used to indicate sleep state (awake or asleep) clearly
to a user in the simplest case. The breathing of the subject may be graphically represented
by a bank of lights which turn on and off as the person breathes in and out. For example,
all of the lights will be off at the point of maximum inspiration, and all lights
will be on at the point of maximum expiration. The display may also have a light emitting
diode to indicate the central apnea alarm condition. The heart rate (beats per minute)
and the breathing rate (breaths per minute) can be indicated in numerical or graphical
format on the display. An indicator of whether the person is moving can also be included.
[0032] FIG. 9 is a diagram illustrating how the apparatus and system of this disclosure
can be used in a configuration where the processing and display unit is remote from
the sensor unit, and communication between the two is achieved wirelessly.
[0033] FIG 10 is a diagram illustrating how information may be integrated from the derived
motion m(t), respiratory r(t) and cardiac signals c(t) together to extract an Apnoea-Hypopnoea
index (AHI) by using a classifier model; an algorithm for processing any combination
of the breathing signal, heart-rate and movement signal to form an estimated Apnoea-Hypopnoea
index, and FIG. 11 is a diagram illustrating an algorithm for processing any combination
of the breathing signal, heart-rate and movement signal to form an estimated AHI including
using only measured and/or derived respiratory effort of a human subject.
[0034] FIG. 12 illustrates an example output of the epoch labels of apnea estimated from
the breathing signal from a night time recording of a subject for which the estimated
AHI was 2.9 and the expert determined AHI was 4.
[0035] FIG. 13 is a block diagram of another embodiment of the apparatus and system of this
disclosure illustrating the possible use of auxiliary sensors such as sound, ultrasound,
infrared, light, and/or relative humidity. It also demonstrates in block diagram format,
a representative schematic of a specific embodiment which includes a transceiver,
a processor, a data logger, a visual display means, an audible indicator, and auxiliary
sensors.
[0036] In one embodiment, a system includes a sensor unit, which can be placed relatively
close to where the subject is sleeping (e.g., on a bedside table) and a monitoring
and display unit through which results can be analyzed, visualized and communicated
to the user. The sensor unit and the display/monitoring unit may be incorporated into
a single stand-alone unit, if required. The unit may contain one or more of the following
features: a non-contact motion sensor for detection of general bodily movement, respiration,
and heart rate; a processing capability to derive parameters such as sleep state,
breathing rate, heart rate, and movement; a display capability to provide visual feedback;
an auditory capability to provide acoustic feedback, e.g., a tone whose frequency
varies with breathing, or an alarm which sounds when no motion is detected; and a
wired or wireless communications capability to transmit acquired data to a separate
unit. This separate unit can carry out the processing, display and auditory capability
mentioned above.
[0037] Additional sensing capabilities can be added to the sensor unit, including a sound
sensor; a sensor for measuring body temperature from a distance (infrared); and sensors
for environment humidity, temperature and light level.
[0038] In one specific embodiment, the motion sensor may include a radio-frequency Doppler
sensor, which can be used to transmit radio-frequency energy (typically in the range
100 MHz to 100 GHz), and which then uses the reflected received signal to construct
a motion signal. The principle by which this works is that a radio-frequency wave
is transmitted from the unit. In this example, the carrier frequency is
fc,
t is time, and θ is an arbitrary phase angle, and
u(
t) is a pulse shape. In a continuous wave system, the magnitude of
u(
t) is always one, and can be omitted from Eq. (1). More generally, the pulse will be
defined as
where T is the period width, and T
p is the pulse width. Where T
p<<T, this becomes a pulsed continuous wave system. In the extreme case, as T
p becomes very short in time, the spectrum of the emitted signal becomes very wide,
and the system is referred to as an ultrawideband (UWB) radar or impulse radar. Alternatively,
the carrier frequency of the RF transmitted signal can be varied (chirped) to produce
a so-called frequency modulated continuous wave (FMCW) system.
[0039] This radio frequency signal may be generated by a transmitter collocated with the
sensor using a local oscillator coupled with circuitry for applying the pulse gating
or, with proper control of signal timing, the transmitter can separate from the receiver/sensor
in a so-called "bistatic" configuration. In the FMCW case, a voltage controlled oscillator
is used together with a voltage-frequency converter to produce the RF signal for transmission.
The coupling of the RF signal to the air may be accomplished using an antenna. The
antenna can be omnidirectional (transmitting power more-or-less equally in all directions)
or directional (transmitting power preferentially in certain directions). It may be
advantageous to use a directional antenna in this system so that transmitted and reflected
energy is primarily coming from one direction. The apparatus, system, and method of
this disclosure is compatible in various embodiments with various types of antenna
such as simple dipole antennas, patch antennas, and helical antennas, and the choice
of antenna can be influence by factors such as the required directionality, size,
shape, or cost. It should be noted that the apparatus and system can be operated in
a manner which has been shown to be safe for human use. The system has been demonstrated
with a total system emitted average power of 1 mW (0 dBm) and lower. The recommended
safety level for RF exposure is 1 mW/cm2. At a distance of 1 meter from a system transmitting
at 0dBm, the equivalent power density will be at least 100 times less than this recommended
limit.
[0040] In all cases, the emitted signal will be reflected off objects that reflect radio
waves (such as the air-body interface), and some of the reflected signal will be received
at a receiver, which can be collocated with the transmitter, or which can be separate
from the transmitter, in a so-called "bistatic" configuration. The received signal
and the transmitted signal can be multiplied together in a standard electronic device
called a mixer (either in an analog or digital fashion). For example, in the CW case,
the mixed signal will equal
where
φ(
t) is the path difference of the transmitted and received signals (in the case where
the reflection is dominated by a single reflective object), and y is the attenuation
experienced by the reflected signal. If the reflecting object is fixed, then
φ(
t) is fixed, and so is
m(
t). In the case of interest to us, the reflecting object (e.g., chest) is moving, and
m(
t) will be time-varying. As a simple example, if the chest is undergoing a sinusoidal
motion due to respiration:
then the mixed signal will contain a component at
fm (as well as a component centred at 2
fc which can be simply removed by filtering). The signal at the output of the low pass
filter after mixing is referred to as the raw sensor signal, and contains information
about motion, breathing and cardiac activity.
[0041] The amplitude of the raw sensor signal is affected by the mean path distance of the
reflected signal, leading to detection nulls and peaks in the sensor (areas where
the sensor is less or more sensitive). This effect can be minimised by using quadrature
techniques in which the transmitter simultaneously transmits a signal 90 degrees out
of phase (the two signals will be referred to as the I and Q components). This will
lead to two reflected signals, which can be mixed, leading eventually to two raw sensor
signals. The information from these two signals can be combined by taking their modulus
(or other techniques) to provide a single output raw sensor signal.
[0042] In the UWB case, an alternative method of acquitting a raw sensor signal may be beneficial.
In the UWB case, the path distance to the most significant air-body interface can
be determined by measuring the delay between the transmitted pulse and peak reflected
signal. For example, if the pulse width is 1 ns, and the distance form the sensor
to the body is 0.5m, then the total time
m(τ) elapsed before a peak reflection of the pulse will be 1/(3×108) s = 3.33 ns. By
transmitting large numbers of pulses (e.g., a 1 ns pulse every 1 µs) and assuming
that the path distance is changing slowly, we can derive a raw sensor signal as the
average of the time delays over that period of time.
[0043] In this way, the sensor, e.g., a radio-frequency sensor, can acquire the motion of
the chest wall, or more generally the part of the body at which the system is aimed.
Directional selectivity can be achieved using directional antennas, or multiple RF
transmitters. A respiration signal acquired in this way using a pulsed continuous
wave system is shown in the top panel of FIG 4.
[0044] Moreover, since the bulk of the reflected energy is received from the surface layer
of the skin, this motion sensor can also obtain the ballistocardiogram, which is the
manifestation of the beating of the heart at the surface of the skin due to changes
in blood pressure with each beat. An example of a surface ballistocardiogram obtained
with an RF motion sensor is shown in FIG 5, together with a reference cardiogram signal
from a finger-mounted pulse oximeter. In the received signal from a sleeping subject,
the sensor will typically have a mixture of a respiration and a cardiac signal, as
well as having motion artefacts. These various signals can be separated by signal
processing using a variety of techniques including digital filtering techniques (e.g.,
a linear bandpass filter of bandwidth 2-10 Hz can be used to extract the cardiac signal
primarily, while a bandpass filter of bandwidth 0.15 to 0.6 Hz can extract the respiration
component). More general digital filtering techniques such as adaptive noise cancellation
or non-linear filters may also be used. This is schematically illustrated in FIG 2.
[0045] As mentioned above, the received signal can include large motion artifacts. This
is due to the fact that the reflected signals from the body can contain more than
one reflection path, and lead to complex signals (for example if one hand is moving
towards the sensor, and the chest is moving away). Such a complex signal in response
to upper body motion is shown in the raw signal illustrated in FIG 2. The reception
of such signals is useful as it can indicate that the upper body is in motion, which
is useful in determining sleep state. The sensor can also be used to detect motion
signals from the lower part of the body (such as involuntary leg jerks) which are
useful in the diagnosis of sleep disorders such as Restless Legs Syndrome or Periodic
Limb Movements.
[0046] In order to improve the qualities of the measured respiration, cardiac, and motion
signals, the physical volume from which reflected energy is collected by the sensor
can be restricted using various methods. For example, the transmission antenna can
be made "directional" (that is, it transmits more energy in certain directions), as
can the receiver antenna. A technique called "time-domain gating" can be used to only
measure reflected signals which arise from signals at a certain physical distance
form the sensor. Frequency domain gating can be used to restrict motions of the reflected
object above a certain frequency.
[0047] In a simple embodiment of the system, a single antenna will be used, with a single
carrier frequency. This antenna will act as both the transmit and receive antenna.
However, in principle, multiple receive and transmit antennas can be used, as can
multiple carrier frequencies. In the case of measurements at multiple frequencies
(e.g., at 500 MHz and 5 GHz) the lower frequency can be used to determine large motions
accurately without phase ambiguity, which can then be subtracted from the higher-frequency
sensor signals (which are more suited to measuring small motion). Using this sensor,
the system collects information from the person, and uses that to determine breathing,
heart rate, and motion information.
[0048] The additional optional sensors can be incorporated as follows. The optional acoustic
sensor in the monitoring is a microphone responsive to sound energy in the range 20-10KHz
(for example), and can be used to determine background noises, and noises associated
with sleeping (e.g. snoring). Background noise cancellation techniques can be used
to emphasise the person's breathing noise, if necessary. The subject's surface temperature
can be measured using an infrared device. Other environmental parameters can be collected
such as temperature, humidity and light level using known sensor technology. In particular,
motion activity can also be collected from an under-mattress piezoelectric sensor,
and this motion signal can then be used as a substitute or to complement the motion
signal obtained from the radio-frequency sensor.
[0049] All of these sensor inputs may be fed into the unit for processing and display purposes,
and for possible transmission to a separate unit (the monitoring unit).
[0050] The system can then use its processing capability to combine the sensor inputs to
provide a number of useful outputs, and to display these outputs in a meaningful manner.
These steps are carried out in the following manner.
[0051] Information about bodily motion is determined in the following way. If the person
moves, there will be a corresponding large change in the received signal from the
non-contact sensor, due to the sudden significant change in the radio-frequency path
length. These "motion events" can be recognised by comparing the energy of the signal
over a short epoch (typically 0.5 to 5 seconds) with the baseline movement seen by
the sensor over a longer period of time (refer to FIG. 3). If the energy in the epoch
exceeds a predetermined threshold relative to the proceeding time, then that epoch
is judged to be an "activity event" and is marked as such. The amount by which the
energy exceeds the threshold can be used to weight the amplitude of the activity of
the event. Alternatively, motion can be detected by counting "threshold-crossings"
- the number of times the signal passes through a preset level. This is also called
a zero-crossing technique.
[0052] In that way, a motion profile can be built up of the received signal. By comparison
with a database of previously collected motion profiles, the overall motion can be
classified into categories such as "no motion", "slight motion" or "large motion".
In this regard, the apparatus, system, and method of this disclosure may find application
in physical security situations to detect living beings through a visually opaque
wall, for example.
[0053] Information about respiration can be acquired in the following way. Firstly, the
frequency of respiration is a useful means of characterising breathing patterns as
faster breathing is associated with respiratory distress (for example). Respiratory
frequency can be defined as the number of breaths per minute, e.g., 10 breaths per
minute. Moreover, variability in the respiratory frequency can be a useful indicator
of sleep state. Respiratory frequency is more variable in Rapid-Eye-Movement (REM)
than in non-REM sleep. To calculate respiratory frequency, the signal from the respiratory
signal (as shown in FIG 4) is processed. Respiratory frequency is calculated over
a certain time scale (e.g., 10 seconds or 100 seconds) by taking the power spectral
density estimate of the signal. Conventional techniques for calculating power spectral
density such as the averaged periodogram may be used. If sections of the respiratory
signal have been excessively corrupted by motion, then a technique called Lomb's periodogram
may be used, which can estimate power spectral density with missing sections of data.
Once the power spectral density (PSD) has been calculated, the respiratory frequency
is located by searching for the peak in the PSD in the range 0.1 to 0.8 Hz (which
is the normal range of human breathing frequencies). Since adults typically have lower
respiratory frequencies than infants and young children, the search range can be reduced
to 0.1 to 0.5 Hz (for example). If the power in the peak exceeds the average power
in the rest of the band by a certain amount (e.g., at least 50% stronger than background),
then we recognise that frequency as the respiratory frequency for the epoch. In that
manner, the respiratory frequency of each epoch can be calculated over the period
of measurement.
[0054] The amplitude of the respiration signal is also of importance, and is reflected in
the amplitude of the sensor respiration signal. Amplitude variation is an identifying
feature of a sleep disordered breathing called Cheyne-Stokes respiration, in which
the amplitude of breathing varies from very shallow to very large over a time scale
of typically 60 seconds. The current invention can reliably estimate the amplitude
of the breathing signal over an epoch by taking the square root of the power at and
near the peak of the respiratory power spectral density discussed above. In this way,
the variation of amplitudes over epochs of time can be tracked.
[0055] The periodic nature of the patterns in the respiratory signal are also important
as it can indicate the presence of sleep disorder breathing. Obstructive apnea manifests
itself as repeated patterns of disrupted breathing and recovery breaths over time
scales of typically 60 seconds. The current disclosure can reliably detect these patterns
by calculating a power spectral density (PSD) of the epochs of the breathing signal
and isolating the frequency component in the 0-0.05 Hz bands.
[0056] Obstructive apnea may be detected applying a threshold to these frequency components
and where a component exceeds the threshold then it can be said with high reliability
that obstructive apnea is present. A more accurate way is to use the frequency component
values (or other measures derived from the breathing signal) as an input into a classifier
(for example a linear discriminate classifier) which then output the probability of
apnea having occurred during the epoch. An estimated Apnoea-Hypopnoea index (AHI)
value may be calculated by summing probabilities for each epoch, dividing by the duration
of the recording to estimate the minutes per hour in apnea. An AHI value may then
be calculated by multiplying the minutes-per-hour in apnoea by a predetermined constant.
[0057] In addition to the respiratory information, we can also process the cardiac and movement
information to enhance the accuracy of the system in detecting sleep disordered breathing.
For example, information from the cardiac activity can be used the enhance the classification
accuracy of the respiratory based detector of sleep disordered breathing. Using the
pulse of that time's a set of features are calculated for each epic, which consists
of a plurality of the following PSD of the pulse event time, the standard deviation
of the pulse event times, and the serial correlation of the pulse event times. These
cardiac activity features are processed by a classifier (such as a linear discriminate
classifier) to produce a probability of apnea. Further, information from the activity
can be used to determine when the subject was aroused from sleep by counting the number
of movement ethics per epic and processing this with a linear discriminate classifier
to produce a probability of apnea so as to identify individual apnoeic events.
[0058] The three probabilities (or two or more probabilities if the quality is poor and
no features are calculated for one or more of the breathing, cardiac, or movement
signals) can be combined using a probability combiner (for example, by averaging the
probabilities).
[0059] An estimated Apnea-Hypopnoea Index (API) value may be calculated by averaging the
combined probabilities for each epic and multiplying by the number of epochs per hour
to estimate the minutes per hour in apnea. An AHI value may then be calculated by
multiplying the minutes per hour in apnea by a predetermined linear mapping.
[0060] The apparatus and system of this disclosure has been trained to estimate the AHI
using the respiratory, movement, and heart rate data from 125 subjects who have undergone
a full polysomnogram. The results show that the system can distinguish between patients
with moderate to severe apnea (AHI > 15) from patients free of apnea (AHI < 5) with
an accuracy of greater than 82%.
[0061] It is also of importance to sense when respiration is absent (so called central apnea),
for example, in monitoring human babies. This can be measured by taking the respiratory
amplitude measure defined above over an epoch of interest, and if it falls below a
certain threshold (which determines the sensitivity), then it is said that respiration
is absent. For example, if no respiration is present for an epoch of 15 seconds in
babies, then an alarm can be sounded to alert the user to the central apnea condition.
[0062] Information about cardiac activity may be acquired in the following way. The initial
"cardiac signal" is acquired through bandpass filtering of the raw sensor signal,
using a bandpass filter. The resulting signal is then called the ballistocardiogram.
Each contraction of the heart is associated with a characteristic pulse shape seen
at the surface of the skin. Each pulse shape can then be determined using a simple
technique such as peak finding, or through a more elaborate template matching approach.
In the template matching approach, a template pulse shape (derived from previous recordings)
is correlated with the ballistocardiogram. The points at which the correlation is
highest are determined to be the pulse event times.
[0063] The heart rate can then be determined by counting the number of pulse shapes per
unit time. Other useful parameters such as inter-cardiac intervals can be determined
by calculating the difference between pulse shape times. For example, if the pulse
shape times are [0.1 s, 1.1 s, 2.3 s, 3.1 s, ...] then the corresponding inter-cardiac
intervals are given by 1 s, 1.2 s, and 0.8 s.
[0064] As well as determining respiration rate and amplitude, cardiac rate, and motion,
the system provides for means to combine signals for calculation of further useful
outputs. For example, the system can be sued to determine whether a person is asleep
or not over a defined epoch of measurement. The means for doing so is as follows.
[0065] Data from the respiration, cardiac and motion channels is segmented into epochs of
time. For example, an epoch might consist of readings over 5 seconds or over 5 minutes,
depending on the desired configuration. For each epoch, a set of features are calculated,
which may include one or more of the following conventionally known and determined
features: The count of activities; the mean amplitude of activity counts; the variance
of activity counts; the dominant respiratory frequency; the respiratory power (e.g.,
the integral of the PSD in a region about the dominant respiratory frequency); the
heart rate; the variability of the heart rate; the spectrum of the respiration signal;
and the spectrum of the raw signal.
[0066] Selected features may be fed into a classifier model (such as a conventional linear
discriminant analysis classifier) which will then provide the probability for that
epoch to belong to a certain class of interest. As a specific example, three classes
are known and defined in the art for sleep state: AWAKE, NON-REM SLEEP, REM SLEEP.
Each of these classes may be associated in a probabilistic sense with a preferred
distribution of feature values, and the classifier model uses this statistical fact
to provide a classification output for each epoch. Moreover, probabilities from each
epoch can be further combined to enhance the accuracy of the classification. These
epoch classifications can then be combined over an entire night's recording to provide
a so-called hypnogram, which maps the time period into different sleep stages. An
important parameter that can be derived from the hypnogram is the sleep efficiency,
which is the percentage of time asleep as a fraction of the total time in bed.
[0067] The information can also provide a measure of sleep quality by measuring motion over
the night's sleep. As above, the data is divided into epochs of time, and activity
count features are measured over each epoch. Based on comparison with a previously
collected database, and using the classifier methodology outlined above, each epoch
can then be classed as "no motion", "gentle motion", "moderate motion" or "severe
motion". From these epoch classifications, a sleep quality index can be determined
by counting the number of epochs assigned to each motion class..
[0068] The apparatus, system, and method of this disclosure can also be used to provide
information about the transition between non-REM (rapid eye movement) sleep and REM
sleep, as such transitions are known to typically accompanied by positional changes
and relatively large movements, after a period of relatively low motion.
[0069] Further, the apparatus, system, and method of this disclosure can also be used to
provide a respirogram over the night's recording in a much less intrusive and complicated
manner than is conventionally available. The respirogram is a measure of respiratory
frequency over the night's sleep, and can be calculated by plotting the respiratory
frequency over the entire night's recording.
DISCUSSION OF VARIOUS EMBODIMENTS
[0070] Various embodiments of an apparatus, system, and method of physiological monitoring
are contemplated by this disclosure. The apparatus useful in detecting, analyzing,
and displaying one or more of a respiration parameter, cardiac activity, and bodily
function or movement of a subject includes a processor configured to analyze a signal
reflected from the subject without physical contact with the subject and to derive
measurements of various physiological parameters of the subject, e.g., a human subject.
A display is configured to provide the analyzed and derived measurements to a local
or remote user of the apparatus. The reflected signal is an RF signal.
[0071] In another aspect of this and various embodiments, a sensor may be coupled to the
processor and arranged to receive the signal reflected from the subject. The sensor
and processor are arranged to operate without any direct or indirect physical contact
with the subject. In another aspect of this embodiment, the reflected signal may be
generated by a transmitter collocated with the apparatus. Further, the transmitter
may be configured to generate an RF energy signal compatible for use with a human
subject. A multiplier circuit is arranged to multiply the reflected signal with a
transmitted signal and to output a baseband signal representing respiration, cardiac,
and a bodily function or movement therefrom. Bodily functions may include, for example,
urination of a child which may be detected by small bodily movements of the subject.
[0072] In another aspect of this and various embodiments, the processor may be configured
to recognize periods of bodily motion of the human subject by identifying peaks in
an energy envelope of the baseband signal. Further, the processor may be configured
to recognize periods of bodily motion of the human subject by counting a number of
threshold-crossings of the baseband signal per unit time. In another aspect of this
and various embodiments, a sensor is provided and the processor is configured to receive
the baseband signal and to output a processed signal in response, and the processor
may further be configured to use the processed signal to distinguish breathing activity
of the human subject or cardiac activity of the human subject. The processed signal
may be the output of bandpass, multi bandpass, or signal separation processes implemented
by known digital signal processing techniques, for example, by independent component
analysis.
[0073] In another aspect of this and various embodiments, the processor may be configured
to determine an activity count for a measurement epoch by calculating an energy of
the baseband signal relative to other epochs. Further, the processor may be configured
to run a classifier model so as to determine a Cheyne-Stokes respiration pattern by
processing features obtained from a respiratory signal derived from the baseband signal.
In addition, the processor may be configured to determine an Apnoea-Hypopnoea Index
(AHI) by processing a respiratory signal derived from the baseband signal; the AHI
may be determined solely by a derived respiratory effort parameter. In a related aspect
of this and various embodiments, the processor may be configured to determine a the
sleeping status of the subject by analysis of a motion signal derived from the baseband
signal. In other aspects, the classifier model may be run to determine a sleep state
by combining one or more of motion signals, breathing signals, and cardiac signals
provided by the classifier model. In a further related aspect of this and various
embodiments, the processor may be configured to recognize a central apnea condition
by determining that breathing and motion activity of the subject are below a predetermined
threshold for a period of time. In further aspects, the processor may be configured
to recognize a respiratory distress condition of the human subject by comparing a
derived respiratory frequency with an existing set of respiratory measurements.
[0074] In other aspects of the embodiment, the processor causes a visual or aural indication
device to signal one or more of a sleep status, a breathing parameter, a heart rate,
or a bodily movement of the subject to a user.
[0075] In another embodiment, a system for measuring, analyzing, and displaying one or more
of a respiration parameter, cardiac activity, and bodily movement or function of a
subject includes,
inter alia, a transmitter arrangement configured to propagate a radio frequency signal toward
the subject and a receiver arranged to receive the signal reflected from the subject.
A processor is arranged to analyze the reflected signal to produce measurements of
one or more of a respiration parameter, cardiac activity, and a bodily movement or
function. A monitor may be used to provide selected information to a local or remote
user of the system by either an audible or visual indication, or both. The system
may further include one or more auxiliary sensors coupled to the processor, e.g.,
one or more of an acoustic sensor, temperature sensor, humidity sensor, and a light
sensor.
[0076] In another embodiment, a method for measuring, analyzing, and displaying one or more
physiological parameters of a subject includes, among other steps, sensing a signal
reflected from the subject and processing and analyzing the reflected signal. The
reflected signal is an RF signal. One or more physiological parameters pertaining
to the subject are derived. The physiological parameters may include one or more of
a respiration parameter, cardiac activity, and bodily movement or function of the
subject. Finally, selected derived information may then be made available to the user,
for example, on a display monitor. In other aspects, an audible alarm may be sounded
in response to a determination that one or more of the physiological parameters is
outside a normal limit. Such physiological parameters may include, for example, an
Apnoea-Hypopnoea Index (AHI) obtained by analyzing a respiratory signal derived from
the reflected radio signal.
[0077] In a related embodiment, a computer-readable medium contains computer instructions
thereon which, when executed on a computer, carry out the functions of measuring,
analyzing, and displaying one or more physiological parameters of a living subject
by processing and analyzing a signal reflected from the living subject; deriving said
one or more physiological parameters pertaining to said living subject, said one or
more physiological parameters comprising one or more of a respiration parameter, cardiac
activity, and bodily movement or function of a subject; and making selected derived
information available to a user.
[0078] In another aspect of this and various embodiments, the reflected signal may be processed
and analyzed by using a baseband signal obtained by multiplying a transmitted signal
by the reflected signal. The baseband signal may be analyzed with a classifier and
an activity count may then be determined in response to the classification result.
The determined activity count to determine said one or more physiological parameters.
EXPERIMENTAL RESULTS
[0079] One example of the application of the apparatus, system, and method of this disclosure
is in the detection and diagnosis of various sleep disorders.
[0080] Background: Actimetry is a widely accepted technology for the diagnosis and monitoring of sleep
disorders such as insomnia, circadian sleep/wake disturbance, and periodic leg movement.
In this study we investigated a sensitive non-contact biomotion sensor to measure
actimetry and compare its performance to wrist-actimetry. A data corpus consisting
of twenty subjects (ten normals, ten with sleep disorders) was collected in the unconstrained
home environment with simultaneous non-contact sensor and ActiWatch® actimetry recordings
used as a baseline standard. The aggregated length of the data was 151 hours. The
non-contact sensor signal was mapped to actimetry using 30 second epochs and the level
of agreement with the ActiWatch® actimetry determined. Across all twenty subjects,
the sensitivity and specificity was 79% and 75% respectively. In addition, it was
shown that the non-contact sensor can also measure breathing and breathing modulations.
The results of this study indicate that the non-contact sensor is a highly convenient
alternative to wrist-actimetry as a diagnosis and screening tool for sleep studies.
Furthermore, as the non-contact sensor measures breathing modulations, it can additionally
be used to screen for respiratory disturbances in sleep caused by sleep apnea and
chronic obstructive pulmonary disease (COPD).
[0081] Sleep assessment can be based on many different types of signals. Existing methods
to measure these signals, include polysomnography (PSG), actigraphy, and sleep diaries.
PSG, the "gold standard" for sleep assessment, may be impractical for some applications,
particularly for usage in the home. It can be both intrusive and expensive.
[0082] Actimetry is a mature technology, developed over the last 25 years. An actimeter
is a wearable motion sensing and data logging device that records the motion data
continuously for days, weeks, or even longer. The actimetry monitor is generally placed
on the non-dominant wrist, leg, or sometimes the trunk. The digitized actimetry signal
can be processed on a computer and used to diagnose and monitor sleep disorders such
as insomnia, circadian sleep/wake disturbance, and periodic leg movement (PLM). Actigraphy
is not considered to be as reliable as full PSG studies for the diagnosis of sleep
disorders, but due to suitability to record continuously for long periods of time,
its convenience and its low-cost, it is a very useful screening device. It is considered
more reliable than patient sleep logs.
[0083] A brief description of conventional actimetry technology is given here. A sensitive
linear accelerometer is employed to capture movements. The movement is bandpass filtered
(typically 0.25 to 2-3Hz). This eliminates very slow movements and fast human movements
such as shivers and involuntary tremors. Voluntary human movements rarely exceed 3-4Hz.
[0084] The motion is transduced into an analog electrical signal and digitized. The movement
counts are accumulated over an epoch, the length of which is generally user programmable.
The analog signal can be digitized using three methods, a) time above a threshold,
b) number of zero crossings, or c) digital integration. The time above threshold method
accumulates the amount of time the analog signal is above a pre-determined threshold
during the epoch. An example threshold might be 0.2g (g=9.8 m/s2). Two issues with
this method are, (a) that there is a saturation effect because the signal amplitude
above the threshold is ignored and, (b) movement acceleration is not measured.
[0085] The zero crossings method counts the number of times that the actimetry signal level
crosses the zero line during an epoch. Three issues with this method are that, (a)
movement amplitude is not captured, (b) movement acceleration is not measured, and,
(c) it is susceptible to large invalid count readings due to high frequency artifacts.
The digital integration method samples the analog actimetry signal at a high rate.
The area under the curve is then calculated. Both amplitude and acceleration information
is captured. The digital integration method has been found to outperform the time
above threshold and zero crossing methods for identifying movement.
[0086] Actigraphy is often reported as counts but it is important to stress that different
hardware devices and different actimetry algorithms can produce very different counts
for the same actimetry. Thus, a direct comparison between ActiWatch® actigraphy and
actimetry derived from the non-contact sensor is difficult. An alternative method
is to compare the temporal location of actimetry. This would allow the capture of
false positives and false negatives.
[0087] Non-contact radar technology sensors can monitor respiratory, movement, and even
cardiac signals in an un-intrusive manner. Non-contact sensors offer a number of advantages
over existing technologies in that 1) there is no contact with the subject, 2) the
cost of the sensor is very low, and 3) the sensors are very portable.
[0088] Method: Simultaneous actimetry and non-contact sensor recordings were recorded for twenty
subjects consisting of twelve females and eight males, with a mean age of 46.7 years
(SD 21.3). Nine of the subjects were classified as healthy. For the other eleven subjects,
six had severe sleep apnea, two had moderate sleep apnea, one had COPD, one had childhood
obesity, and one suffered from insomnia. The recordings were made in the unconstrained
home environment under a doctor's supervision.
TABLE I: DETAILS OF THE SUBJECTS IN THE TEST CORPUS |
Record Number |
Age (years) |
Sex |
Health Status |
Length (hours) |
1 |
36 |
F |
Healthy |
8.04 |
2 |
29 |
F |
Healthy |
8.33 |
3 |
67 |
F |
Moderate Sleep Apnea |
7.67 |
4 |
30 |
F |
Healthy |
4.38 |
5 |
49 |
M |
Healthy |
6.89 |
6 |
30 |
F |
Healthy |
7.36 |
7 |
31 |
F |
Healthy |
6.11 |
8 |
79 |
F |
COPD |
7.53 |
9 |
8 |
F |
Childhood Obesity |
8.06 |
10 |
23 |
F |
Healthy |
8.84 |
11 |
34 |
F |
Healthy |
8.74 |
12 |
30 |
F |
Healthy |
7.56 |
13 |
34 |
M |
Moderate Sleep Apnea |
6.33 |
14 |
69 |
M |
Severe Sleep Apnea |
6.72 |
15 |
79 |
F |
Insomnia |
8.19 |
16 |
58 |
M |
Severe Sleep Apnea |
8.02 |
17 |
49 |
M |
Severe Sleep Apnea |
8.16 |
18 |
51 |
M |
Severe Sleep Apnea |
7.82 |
19 |
77 |
M |
Severe Sleep Apnea |
7.92 |
20 |
72 |
M |
Severe Sleep Apnea |
7.97 |
[0089] Actimeter (ActiWatch®): The Actiwatch® (registered trademark of Mini Mitter Company) is a long-term activity
monitoring device used in this study to provide a baseline of activity counts. It
is cordless, and data is transferred to the PC via a close proximity RF link. The
Actiwatch® contains a sensor capable of detecting acceleration in two planes. It is
sensitive to 0.01g, and integrates the degree and speed of motion and produces an
electrical current with varying magnitude. An increased degree of speed and motion
produces an increase in voltage. The watch converts this signal and stores it as activity
counts. The maximum sampling rate is 32 Hz. For this study, the watch was placed on
the non-dominant wrist and set to record the number of activity counts during 15 second
intervals (epochs).
[0090] Non-contact Sensor: The non-contact sensor employed in this study is a multichannel biomotion sensor
employing 5.8GHz Doppler radar using a modulation system that limits both the maximum
and minimum range. Quadrature operation eliminates range-dependent sensing nulls.
The baseband inphase (I) and quadrature (Q) signals were filtered using analog active
filters with bandwidths (0.05- 1.6) Hz and (1-5) Hz. The emitted power is very low
- less than 10mW.
[0091] Non-contact Sensor Data Logger: The design of the non-contact biomotion logger used in this study shares some of
the benefits of existing actimeters including convenience of use, light weight, portability,
cheap, low power usage, non-intrusive, and the capacity to record for several days
or even for weeks. The data logger manufactured by BiancaMed Ltd. incorporates all
of the aforementioned characteristics, and it can be powered by the electric mains
or battery. It is a standalone device which records data from an internal non-contact
sensor to an SD flash card for easy transfer to a PC for analysis. It is capable of
logging continuously for weeks with standard off-the-shelf SD cards (upto 4GB), as
used in digital cameras. It contains an independent battery-powered clock which tags
the movement data with accurate time information and digitizes the sensor channels
at 50Hz with 10-bit resolution. The user places the data logger no more than 1 meter
from the bed, between 0.25 to 0.5 meters above the height of the mattress, and facing
towards the torso of the subject. For the detection of movement (actimetry), positioning
of the logger has been found not to be crucial. For detection of breathing, the data
logger is more sensitive to positioning however, experiments show that if placed within
the above limits, good signals are obtained.
[0092] Non-contact to Actimetry Mapping: The I and Q channels were combined when doing breathing analysis, however, for actimetry
data, it is sufficient to use only one channel (either I or Q). The mapping from the
non-contact sensor to actimetry is carried out as follows:
- 1) The first stage is a digital band pass filter with passband (1.5, 4.6) Hz, stopband
(0.7, 4.9) Hz, 3dB passband, and stopband attenuation of 50dB; implemented as a 7th
order Butterworth filter. This filter attenuates the breathing frequencies, thus emphasizing
the movement frequencies.
- 2) The respiration signal is then removed with a sort filter.
- 3) Finally, the signal is thresholded and summed into non-overlapping two second bins
to give an actimetry count. The two second epochs can then be downsampled to the appropriate
epoch and compared with wrist based actimetry.
[0093] Due to varying clock offsets between the ActiWatch® and data logger, the actimetry
and non-contact sensor recordings were aligned manually. After alignment, the signals
were truncated so that only data that were recorded simultaneously were retained.
The length of each aligned and truncated set of recordings is given in Table 1. The
average length is 7.53 hours with an aggregated length of 151 hours across all 20
recordings.
[0094] Performance Measure: The performances measures are epoch based. The actimetry counts were aggregated
into 30 second epochs for both the ActiWatch® and the non-contact actimetry. For each
epoch, counts greater than one were quantized to one and a comparison made between
the quantized counts of the ActiWatch® and the non-contact sensor, i.e., the comparison
measures the accuracy of temporal activity location, rather than magnitude of the
actimetry. Table 2 shows the four possible states that can arise when comparing the
reference epoch (ActiWatch® actimetry) with the non-contact actimetry epoch, TN, FN,
FP, and TP refer to true negative, false negative, false positive, and true positive,
respectively. The sensitivity (the probability that an epoch with actimetry is detected
by the non-contact actimetry mapping) is defined as:
and the specificity (the probability that the an epoch without actimetry is labeled
the same by the non-contact actimetry mapping) is defined as:
TABLE II: THE FOUR POSSIBLE COMPARATIVE STATES THAT CAN ARISE BETWEEN ACTIWATCH® ACTIMETRY
AND NON-CONTACT ACTIMETRY, BASED ON QUANTIZED EPOCH ACTIMETRY COUNTS |
|
Non-contact Actimetry |
0 |
1 |
ActiWatch® |
0 |
TN |
FP |
Actimetry |
1 |
FN |
TP |
[0095] Results: FIG. 14 provides experimental results from a non-contact sensor recording for Record
Number 2 (top axis) with the actimetry recording on the bottom axis in which the signals
have been aligned and truncated, and in which the middle axis shows the non-contact
signal mapped to ActiWatch® actimetry. From FIG. 14, it can be seen that the non-contact
and ActiWatch® actimetry agree very well in temporal location and also in magnitude.
Table III gives the sensitivity and specificity for each of the twenty comparisons
of the noncontact with ActiWatch® actimetry.
TABLE III: EPOCH BASED PERFORMANCE MEASURES FOR EACH OF THE RECORDINGS |
Record Number |
TP |
FN |
FP |
TN |
Sen (%) |
Spec (%) |
1 |
64 |
13 |
107 |
783 |
83 |
88 |
2 |
54 |
35 |
68 |
845 |
61 |
93 |
3 |
94 |
34 |
329 |
465 |
73 |
59 |
4 |
47 |
3 |
81 |
396 |
94 |
83 |
5 |
75 |
16 |
26 |
711 |
82 |
96 |
6 |
18 |
37 |
32 |
798 |
33 |
96 |
7 |
97 |
73 |
59 |
506 |
57 |
90 |
8 |
191 |
67 |
97 |
550 |
74 |
85 |
9 |
85 |
18 |
136 |
729 |
83 |
84 |
10 |
150 |
5 |
152 |
755 |
97 |
83 |
11 |
106 |
13 |
528 |
404 |
89 |
43 |
12 |
33 |
7 |
26 |
842 |
83 |
97 |
13 |
35 |
6 |
361 |
360 |
85 |
50 |
14 |
59 |
15 |
71 |
663 |
80 |
90 |
15 |
408 |
54 |
431 |
91 |
88 |
17 |
16 |
43 |
5 |
72 |
844 |
90 |
92 |
17 |
87 |
20 |
229 |
645 |
81 |
74 |
18 |
155 |
46 |
384 |
355 |
77 |
48 |
19 |
179 |
38 |
265 |
470 |
82 |
64 |
20 |
208 |
8 |
284 |
458 |
96 |
62 |
Mean |
109 |
26 |
187 |
584 |
79 |
75 |
[0096] Discussion: Across all twenty subjects, the sensitivity and specificity were 79% and 75% respectively.
The non-contact sensor monitors motion over all of the body will thus registers more
motion than a single non-dominant wrist positioned ActiWatch®. This may explain the
lower specificity value. The sensor also proved to be very reliable, convenient and
non-invasive. There were no signal quality or equipment set up issues. None of the
subjects reported being disturbed by the sensor. The results of this study show that
the non-contact sensor can reliably quantify actimetry. Thus, established actimetry
based sleep algorithms can be deployed on non-contact based actimetry data and, for
example, sleep efficiency can be estimated. A full PSG was not carried out for this
study, and hence expert annotated EEG based sleep staging was not possible.
[0097] Due to the lack of expert sleep staging, the sleep efficiencies from the Actiwatch®
and non-contact- actimetry were not compared at this time. Our results demonstrate
that the non-contact sensor can reliably measure the breathing signal, for example,
a spectrogram (not shown) of an overnight non-contact sensor signal and the breathing
frequencies of approximately 0.3Hz (18 breaths per minute) were readily ascertainable.
Additionally, a sample non-contact breathing signal taken from a subject with mild
sleep apnea provides evidence in the modulations in the breathing signal that apnea
is present, and this shows that the apparatus, system, and method of this disclosure,
can not only be used as an actimeter, but also can be employed to automatically screen
for respiratory disturbances during sleep such as occurs during sleep apnea and COPD.
[0098] Conclusion: Thus, it has been demonstrated in one example application that non-contact based
actigraphy can capture equivalent information to that of conventional wrist based
actigraphy. Furthermore, the non-contact biomotion sensor is a richer source of physiological
information. Actigraphy is a single modality signal, whereas, the non-contact biomotion
sensor can capture both actigraphy and respiration information. The non-contact sensor
also proved to be highly convenient and unobtrusive. Even though this demonstration
was conducted using an RF signal, other signal types may be used, e.g., ultrasound,
infrared, or visible light.
STATEMENT OF INDUSTRIAL APPLICABILITY
[0099] The apparatus, system and method of this disclosure finds utility in non-invasive,
non-contact monitoring and analysis of physiological signs of humans or other living
subjects such as respiration and cardiac activity. This disclosure also has applications
to sleep monitoring, stress monitoring, health monitoring, intruder detection, and
physical security.
1. System, das beim Detektieren, Analysieren und Anzeigen von Atmung, Herztätigkeit und/oder
Körperfunktion oder -bewegung eines lebenden Subjekts von Nutzen ist, wobei das System
aufweist:
einen Sender, der so konfiguriert ist, dass er ein gepulstes kontinuierliches Radiofrequenzsignal
erzeugt;
einen kontaktlosen Sensor oder Empfänger, der so angeordnet ist, dass er ohne direkten
oder indirekten körperlichen Kontakt mit dem Subjekt ein gesendetes gepulstes kontinuierliches
Radiofrequenzsignal empfängt, das vom lebenden Subjekt reflektiert wurde;
eine Multiplikationsschaltung, die so angeordnet ist, dass sie das empfangene gepulste
kontinuierliche Radiofrequenzsignal, das vom Subjekt reflektiert wurde, mit einem
Teil des gesendeten gepulsten kontinuierlichen Radiofrequenzsignals multipliziert
und ein Basisbandsignal ausgibt, das Informationen über die Atmung, Herztätigkeit
und/oder Körperfunktion oder -bewegung enthält;
einen Prozessor, der so konfiguriert ist, dass er das Basisbandsignal analysiert und
Messungen der Atmung, Herztätigkeit und/oder Körperfunktion oder -bewegung daraus
ableitet; und eine Anzeige, die so konfiguriert ist, dass sie analysierte und abgeleitete
Messungen bereitstellt; dadurch gekennzeichnet, dass der Prozessor ferner so konfiguriert ist, dass er High-Level-Ausgangsinformationen
auf der Grundlage der analysierten und abgeleiteten Messungen ableitet, wobei die
abgeleiteten High-Level-Ausgangsinformationen einen Schlafzustand und/oder Schlafstörungen
aufweisen; und
die Anzeige ferner so konfiguriert ist, dass sie abgeleitete High-Level-Ausgangsinformationen
für einen lokalen oder fernen Benutzer des Systems bereitstellt.
2. System nach Anspruch 1, wobei der kontaktlose Sensor mit dem Prozessor gekoppelt ist.
3. System nach Anspruch 2, wobei der kontaktlose Sensor und Prozessor beide so angeordnet
sind, dass sie ohne direkten oder indirekten körperlichen Kontakt mit dem Subjekt
arbeiten.
4. System nach Anspruch 1, wobei der Sender mit dem kontaktlosen Sensor oder Empfänger
gemeinsam angeordnet ist.
5. System nach Anspruch 1, wobei der Prozessor so konfiguriert ist, dass er Perioden
von Körperbewegung des Subjekts durch Identifizieren von Peaks in einer Energiehüllkurve
des Basisbandsignals erkennt.
6. System nach Anspruch 1, wobei der Prozessor so konfiguriert ist, dass er Perioden
von Körperbewegung des Subjekts durch Zählen einer Anzahl von Schwellwertdurchgängen
des Basisbandsignals pro Zeiteinheit erkennt.
7. System nach Anspruch 1, wobei der Prozessor ferner so konfiguriert ist, dass er ein
verarbeitetes Signal ausgibt und das verarbeitete Signal verwendet, um Atemtätigkeit
des Subjekts zu unterscheiden.
8. System nach Anspruch 1, wobei der Prozessor ferner so konfiguriert ist, dass er ein
verarbeitetes Signal ausgibt und das verarbeitete Signal verwendet, um eine Herztätigkeit
des Subjekts zu unterscheiden.
9. System nach Anspruch 1, wobei der Prozessor so konfiguriert ist, dass er eine Tätigkeitszählung
für einen Messzeitraum durch Berechnen einer Energie des Basisbandsignals relativ
zu anderen Zeiträumen bestimmt.
10. System nach Anspruch 1, wobei der Prozessor so konfiguriert ist, dass er ein Klassifizierungsmodell
ausführt und ein Cheyne-Stokes-Atemmuster durch Verarbeiten von Merkmalen bestimmt,
die anhand eines Atemsignals erhalten werden, das aus dem Basisbandsignal abgeleitet
wird.
11. System nach Anspruch 1, wobei der Prozessor so konfiguriert ist, dass er einen Apnoe-Hypopnoe-Index
(AHI) durch Verarbeiten eines Atemsignals bestimmt, das aus dem Basisbandsignal abgeleitet
wird.
12. System nach Anspruch 11, wobei der AHI ausschließlich durch einen abgeleiteten Atemanstrengungsparameter
bestimmt wird.
13. System nach Anspruch 1, wobei der Prozessor so konfiguriert ist, dass er ein Klassifizierungsmodell
ausführt und einen Schlafzustand durch Kombinieren von Bewegungssignalen, Atemsignalen
und/oder Herzsignalen bestimmt, die durch das Klassifizierungsmodell bereitgestellt
werden.
14. Verfahren zum Messen, Analysieren und Anzeigen eines oder mehrerer physiologischer
Parameter eines lebenden Subjekts, wobei das Verfahren aufweist:
Senden eines gepulsten Radiofrequenzsignals zum lebenden Subjekt;
Erfassen eines resultierenden Signals, das vom lebenden Subjekt reflektiert wird;
Verarbeiten und Analysieren des reflektierten Signals, wobei das reflektierte Signal
mit dem gesendeten gepulsten Radiofrequenzsignal multipliziert wird, um ein Basisbandsignal
zu erhalten, das Informationen über die Atmung, Herztätigkeit und/oder Körperfunktion
oder -bewegung enthält;
Ableiten eines oder mehrerer physiologischer Parameter, die das lebende Subjekt betreffen,
aus dem Basisbandsignal, wobei der eine oder die mehreren physiologischen Parameter
einen Atemparameter, Herztätigkeit und/oder Körperbewegung oder -funktion des Subjekts
aufweisen;
dadurch gekennzeichnet, dass das Verfahren ferner aufweist:
Ableiten von High-Level-Ausgangsinformationen auf der Grundlage des einen oder der
mehreren physiologischen Parameter, wobei die abgeleiteten High-Level-Ausgangsinformationen
einen Schlafzustand und/oder Schlafstörungen aufweisen; und
Bereitstellen ausgewählter abgeleiteter High-Level-Informationen für einen Benutzer.
15. Verfahren nach Anspruch 14, das ferner aufweist: Analysieren des Basisbandsignals
mit einem Klassifizierer und Bestimmen einer Tätigkeitszählung als Reaktion auf ein
Klassifizierungsergebnis.
16. Verfahren nach Anspruch 14, wobei die physiologischen Parameter einen Apnoe-Hypopnoe-Index
(AHI) aufweisen, der durch Analysieren eines Atemsignals bestimmt wird, das aus dem
vom lebenden Subjekt reflektierten resultierenden Signal abgeleitet wird.